An Optimal Feature Subset Selection for Leaf Analysis
This paper describes an optimal approach for feature
subset selection to classify the leaves based on Genetic Algorithm
(GA) and Kernel Based Principle Component Analysis (KPCA). Due
to high complexity in the selection of the optimal features, the
classification has become a critical task to analyse the leaf image
data. Initially the shape, texture and colour features are extracted
from the leaf images. These extracted features are optimized through
the separate functioning of GA and KPCA. This approach performs
an intersection operation over the subsets obtained from the
optimization process. Finally, the most common matching subset is
forwarded to train the Support Vector Machine (SVM). Our
experimental results successfully prove that the application of GA
and KPCA for feature subset selection using SVM as a classifier is
computationally effective and improves the accuracy of the classifier.
[1] A.Kadir,L.E. Nugroho, A. Susanto and P.I. Santosa, A Comparative
Experiment of Several Shape Methods in Recognizing Plants,
International Journal of Computer Science and Information Technology
(IJCSIT), Vol 3, No 3, P.256-263, June 2011.
[2] Abdul Kadir, Lukito Edi Nugroho, Adhi Susanto, Paulus Insap Santosa,
Leaf Classification Using Shape, Color, and Texture Features,
International Journal of Computer Trends and Technology, P.224-
230,2011.
[3] Jyotismita Chaki, Ranjan Parekh, Plant Leaf Recognition using Shape
based Features and Neural Network classifiers, International Journal of
Advanced Computer Science and Applications (IJACSA), Vol. 2, No.
10, P. 41-47, 2011.
[4] Wahyu Wibowo, Hugh E. Williams, Simple and Accurate Feature
Selection for Hierarchical Categorisation, ACM Digital library, 2002.
[5] P. Tzionas, S.E. Papadakis, and D. Manolakis, "Plant leaves
classification based on morphological features and a fuzzy surface
selection technique", in Proceeding of International Conference on
Technology and Automation, Thessaloniki, Greece, P. 365-370, 2005.
[6] Xiaodong Zheng, Xiaojie Wang, Leaf Vein Extraction Based on Grayscale
Morphology, I.J. Image, Graphics and Signal Processing, Vol.2,
2P.25-31,2010.
[7] N. Kumar, S. Pandey, A. Bhattacharya, and P. S. Ahuja, "Do leaf
surface characteristics affect agrobacterium infection in tea J. Biosci.,
vol. 29, no. 3, P. 309-317, 2004.
[8] G. Guo, S. Li, and K. Chan, "Support vector machines for face
recognition," Image and Vision Computing, vol. 19, no. 9, P. 631-638,
2001.
[9] S. Papadakis, P. Tzionas, V. Kaburlazos, and J. Theocharis, "A genetic
based approach to the Type I structure identification problem,"
Informatica, vol. 5, no. 3, 2005.
[10] Yan Li, Zheru Chi, and David D. Feng, "Leaf Vein Extraction Using
Independent Component Analysis," 2006 IEEE Conference on Systems,
Man and Cybernetics, Vol. 5, Taipei, P. 3890-3894,2006.
[11] Chomtip Pornpanomchai, Chawin Kuakiatngam Pitchayuk
Supapattranon, and Nititat Siriwisesokul,, Leaf and Flower Recognition
System (e-Botanist), International Journal of Engineering and
Technology (IACSIT), Vol.3, No.4, ,P.347-351, 2011.
[12] B.Sathya Bama et.al., Content Based Leaf Image Retrieval (CBLIR)
Using Shape, Color and Texture Features, Indian Journal of Computer
Science and Engineering (IJCSE), Vol. 2 ,No. 2 ,P. 202-211,2011.
[13] Maliheh Shabanzade, Morteza Zahedi and Seyyed Amin Aghvami,
Combination of Local Descriptors and Global Features for Leaf
Recognition, Signal and Image Processing : An International Journal
(SIPIJ) Vol.2, No.3, P. 23-31,2011.
[14] R. Sinan Tumen1, M. Emre Acer2 and T. Metin Sezgin1, Feature
Extraction and Classifier Combination for Image-based Sketch
Recognition, EUROGRAPHICS Symposium on Sketch-Based
Interfaces and Modeling , P.1-8,2010.
[15] Chomtip Pornpanomchai, Supolgaj Rimdusit, Piyawan Tanasap and
Chutpong Chaiyod, Thai Herb Leaf Image Recognition System
(THLIRS), Kasetsart J. (Nat. Sci.) , Vol.45, P. 551 - 562 ,2011.
[16] Krzyszt Michalak, Halina Kwasnicka, Correlation-Based Feature
Selection Strategy in Classification Problems, Int. J. Appl. Math.
Comput. Sci., Vol. 16, No. 4, P.503-511, 2006.
[17] Qisong Chen, Xiaowei Chen and Yun Wu, Optimization Algorithm
with Kernel PCA to Support Vector Machines for Time Series
Prediction, Journal of Computers, Vol. 5, NO. 3, P.380-387, 2010.
[18] Shanwen Zhang and Kwok-Wing Chau, Dimension Reduction Using
Semi-Supervised Locally Linear Embedding for Plant Leaf
Classification, ICIC 2009, LNCS 5754, P. 948-955, 2009.
[19] Debdoot Sheet and Jyotirmoy Chatterjee, Hrushikesh Garud, Feature
Usability Index and Optimal Feature Subset Selection, International
Journal of Computer Applications, Vol.12, No.2, P.29-37, 2010.
[20] D S Guru, Y. H. Sharath, S. Manjunath, Texture Features and KNN in
Classification of Flower Images, IJCA Special Issue on "Recent Trends
in Image Processing and Pattern Recognition", P.21-29, 2010.
[21] Minh Hoai Nguyen, Fernando De la Torre, Optimal Feature Selection
for Support Vector Machines, Pattern Recoginition, P. 1-25, 2009.
[22] Yijuan Lu, Ira Cohen, Xiang Sean Zhou, Qi Tian, Feature Selection
Using Principal Feature Analysis, ACM Multimedia, September 23-29,
2007.
[23] Amaro Lima, Heiga Zen, Yoshihiko, Keiichi Tokuda,Tadashi Kitamura,
Members, and Fernando G. Resende, Applying Sparse KPCA for
Feature Extraction in Speech Recognition, IEICE TRANS. INF. &
SYST., Vol.E88-D, No.3, P. 401-402, 2010.
[1] A.Kadir,L.E. Nugroho, A. Susanto and P.I. Santosa, A Comparative
Experiment of Several Shape Methods in Recognizing Plants,
International Journal of Computer Science and Information Technology
(IJCSIT), Vol 3, No 3, P.256-263, June 2011.
[2] Abdul Kadir, Lukito Edi Nugroho, Adhi Susanto, Paulus Insap Santosa,
Leaf Classification Using Shape, Color, and Texture Features,
International Journal of Computer Trends and Technology, P.224-
230,2011.
[3] Jyotismita Chaki, Ranjan Parekh, Plant Leaf Recognition using Shape
based Features and Neural Network classifiers, International Journal of
Advanced Computer Science and Applications (IJACSA), Vol. 2, No.
10, P. 41-47, 2011.
[4] Wahyu Wibowo, Hugh E. Williams, Simple and Accurate Feature
Selection for Hierarchical Categorisation, ACM Digital library, 2002.
[5] P. Tzionas, S.E. Papadakis, and D. Manolakis, "Plant leaves
classification based on morphological features and a fuzzy surface
selection technique", in Proceeding of International Conference on
Technology and Automation, Thessaloniki, Greece, P. 365-370, 2005.
[6] Xiaodong Zheng, Xiaojie Wang, Leaf Vein Extraction Based on Grayscale
Morphology, I.J. Image, Graphics and Signal Processing, Vol.2,
2P.25-31,2010.
[7] N. Kumar, S. Pandey, A. Bhattacharya, and P. S. Ahuja, "Do leaf
surface characteristics affect agrobacterium infection in tea J. Biosci.,
vol. 29, no. 3, P. 309-317, 2004.
[8] G. Guo, S. Li, and K. Chan, "Support vector machines for face
recognition," Image and Vision Computing, vol. 19, no. 9, P. 631-638,
2001.
[9] S. Papadakis, P. Tzionas, V. Kaburlazos, and J. Theocharis, "A genetic
based approach to the Type I structure identification problem,"
Informatica, vol. 5, no. 3, 2005.
[10] Yan Li, Zheru Chi, and David D. Feng, "Leaf Vein Extraction Using
Independent Component Analysis," 2006 IEEE Conference on Systems,
Man and Cybernetics, Vol. 5, Taipei, P. 3890-3894,2006.
[11] Chomtip Pornpanomchai, Chawin Kuakiatngam Pitchayuk
Supapattranon, and Nititat Siriwisesokul,, Leaf and Flower Recognition
System (e-Botanist), International Journal of Engineering and
Technology (IACSIT), Vol.3, No.4, ,P.347-351, 2011.
[12] B.Sathya Bama et.al., Content Based Leaf Image Retrieval (CBLIR)
Using Shape, Color and Texture Features, Indian Journal of Computer
Science and Engineering (IJCSE), Vol. 2 ,No. 2 ,P. 202-211,2011.
[13] Maliheh Shabanzade, Morteza Zahedi and Seyyed Amin Aghvami,
Combination of Local Descriptors and Global Features for Leaf
Recognition, Signal and Image Processing : An International Journal
(SIPIJ) Vol.2, No.3, P. 23-31,2011.
[14] R. Sinan Tumen1, M. Emre Acer2 and T. Metin Sezgin1, Feature
Extraction and Classifier Combination for Image-based Sketch
Recognition, EUROGRAPHICS Symposium on Sketch-Based
Interfaces and Modeling , P.1-8,2010.
[15] Chomtip Pornpanomchai, Supolgaj Rimdusit, Piyawan Tanasap and
Chutpong Chaiyod, Thai Herb Leaf Image Recognition System
(THLIRS), Kasetsart J. (Nat. Sci.) , Vol.45, P. 551 - 562 ,2011.
[16] Krzyszt Michalak, Halina Kwasnicka, Correlation-Based Feature
Selection Strategy in Classification Problems, Int. J. Appl. Math.
Comput. Sci., Vol. 16, No. 4, P.503-511, 2006.
[17] Qisong Chen, Xiaowei Chen and Yun Wu, Optimization Algorithm
with Kernel PCA to Support Vector Machines for Time Series
Prediction, Journal of Computers, Vol. 5, NO. 3, P.380-387, 2010.
[18] Shanwen Zhang and Kwok-Wing Chau, Dimension Reduction Using
Semi-Supervised Locally Linear Embedding for Plant Leaf
Classification, ICIC 2009, LNCS 5754, P. 948-955, 2009.
[19] Debdoot Sheet and Jyotirmoy Chatterjee, Hrushikesh Garud, Feature
Usability Index and Optimal Feature Subset Selection, International
Journal of Computer Applications, Vol.12, No.2, P.29-37, 2010.
[20] D S Guru, Y. H. Sharath, S. Manjunath, Texture Features and KNN in
Classification of Flower Images, IJCA Special Issue on "Recent Trends
in Image Processing and Pattern Recognition", P.21-29, 2010.
[21] Minh Hoai Nguyen, Fernando De la Torre, Optimal Feature Selection
for Support Vector Machines, Pattern Recoginition, P. 1-25, 2009.
[22] Yijuan Lu, Ira Cohen, Xiang Sean Zhou, Qi Tian, Feature Selection
Using Principal Feature Analysis, ACM Multimedia, September 23-29,
2007.
[23] Amaro Lima, Heiga Zen, Yoshihiko, Keiichi Tokuda,Tadashi Kitamura,
Members, and Fernando G. Resende, Applying Sparse KPCA for
Feature Extraction in Speech Recognition, IEICE TRANS. INF. &
SYST., Vol.E88-D, No.3, P. 401-402, 2010.
@article{"International Journal of Information, Control and Computer Sciences:51670", author = "N. Valliammal and S.N. Geethalakshmi", title = "An Optimal Feature Subset Selection for Leaf Analysis", abstract = "This paper describes an optimal approach for feature
subset selection to classify the leaves based on Genetic Algorithm
(GA) and Kernel Based Principle Component Analysis (KPCA). Due
to high complexity in the selection of the optimal features, the
classification has become a critical task to analyse the leaf image
data. Initially the shape, texture and colour features are extracted
from the leaf images. These extracted features are optimized through
the separate functioning of GA and KPCA. This approach performs
an intersection operation over the subsets obtained from the
optimization process. Finally, the most common matching subset is
forwarded to train the Support Vector Machine (SVM). Our
experimental results successfully prove that the application of GA
and KPCA for feature subset selection using SVM as a classifier is
computationally effective and improves the accuracy of the classifier.", keywords = "Optimization, Feature extraction, Feature subset,
Classification, GA, KPCA, SVM and Computation", volume = "6", number = "2", pages = "187-6", }
